{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入必要的各种包\n",
    "import pandas as pd\n",
    "from pandas.core.api import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "from pyreadstat import pyreadstat\n",
    "import mytools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 打开数据文档\n",
    "df0,metadata =pyreadstat.read_sav(R\"企业形象调查原始数据.sav\",apply_value_formats=True)\n",
    "# 使用自定义工具包中的函数读取spss格式文件\n",
    "df1,metadata = mytools.read_spss(R\"企业形象调查原始数据.sav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 清理数据\n",
    "## 清理空白值\n",
    "### 查看所有空白值\n",
    "temp = df1[df1.isnull().T.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 删除指定的空白值\n",
    "\"\"\"\n",
    "## 使用dropna方法删除空值 #card\n",
    "\n",
    "1. `axis`，用于指定操作行还是列。`0`代表行，`1`代表列，默认为`0`。\n",
    "2. `how`，其值为`\"any\"`和`\"all\"`，`\"any\"`表示只要有空值，就删除。`\"all\"`表示一行或一列的所有值为空才删除。默认为`\"any\"`。\n",
    "3. `thresh`，表示保留至少含有`n`个非`na`数值的行或列。默认为`None`。\n",
    "4. `subset`，用来指定在那些列中查找缺失值。例如`df.dropna(subset=['name', 'born'])`表示删除在`'name'`和` 'born'`列含有缺失值的行。默认为`None`。\n",
    "5. `inplace`，表示是否直接在原DataFrame修改。默认为`False`，即不修改原数据框。\n",
    "\"\"\"\n",
    "df2 = df1.dropna(thresh=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 如果要删除所有空值，执行下面语句即可\n",
    "df3 = df2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 删除重复值\n",
    "df4 = df3.drop_duplicates(subset=['问卷编号'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                 0\n",
       "问卷编号       float64\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄         float64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
       "其他吸引因素     float64\n",
       "服务态度需改进    float64\n",
       "送餐速度       float64\n",
       "就餐环境       float64\n",
       "产品价格       float64\n",
       "促销活动       float64\n",
       "产品种类       float64\n",
       "其他改进因素     float64\n",
       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
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       "德克士综合评价   category\n",
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       "肯德基广告接触度  category\n",
       "肯德基就职意愿   category\n",
       "肯德基股票     category\n",
       "肯德基综合评价   category\n",
       "麦当劳企业认知度  category\n",
       "麦当劳广告接触度  category\n",
       "麦当劳就职意愿   category\n",
       "麦当劳股票     category\n",
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      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 检查数据类型\n",
    "\n",
    "### 查看所有变量的数据类型\n",
    "df4.dtypes.to_frame()\n",
    "### 使用dtypes属性可以查看所有变量或指定变量的类型。\n",
    "### 使用to_frame方法的目的在于输出更加整洁的文本，非必须。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "问卷编号         int32\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄         float64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
       "其他吸引因素     float64\n",
       "服务态度需改进    float64\n",
       "送餐速度       float64\n",
       "就餐环境       float64\n",
       "产品价格       float64\n",
       "促销活动       float64\n",
       "产品种类       float64\n",
       "其他改进因素     float64\n",
       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
       "德克士就职意愿   category\n",
       "德克士股票     category\n",
       "德克士综合评价   category\n",
       "肯德基企业认知度  category\n",
       "肯德基广告接触度  category\n",
       "肯德基就职意愿   category\n",
       "肯德基股票     category\n",
       "肯德基综合评价   category\n",
       "麦当劳企业认知度  category\n",
       "麦当劳广告接触度  category\n",
       "麦当劳就职意愿   category\n",
       "麦当劳股票     category\n",
       "麦当劳综合评价   category\n",
       "必胜客企业认知度  category\n",
       "必胜客广告接触度  category\n",
       "必胜客就职意愿   category\n",
       "必胜客股票     category\n",
       "必胜客综合评价   category"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 使用astypes方法设定变量的类型\n",
    "df5 = df4.astype({'问卷编号':'int'})\n",
    "df5.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "男性      211\n",
       "女性      204\n",
       "22.0      1\n",
       "Name: 性别, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "异常值清理\n",
    "\n",
    "类别变量异常值的查看及修改\n",
    "\n",
    "使用value_counts方法可以查看类别变量的取值及次数\n",
    "\"\"\"\n",
    "df5['性别'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性      211\n",
      "女性      204\n",
      "22.0      1\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     138\n",
      "九百到一千二    120\n",
      "一千二以上      85\n",
      "三百到六百      73\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       136\n",
      "德克士        78\n",
      "从不去快餐店     77\n",
      "麦当劳        62\n",
      "必胜客        44\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    188\n",
      "时尚新颖     91\n",
      "中西合璧     88\n",
      "古板单调     32\n",
      "其他类型     17\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般     224\n",
      "不错     139\n",
      "很好      33\n",
      "不好      17\n",
      "0.0      3\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    123\n",
      "只知道名字      120\n",
      "只知道几项       99\n",
      "大致了解        65\n",
      "非常了解         7\n",
      "0.0          2\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     161\n",
      "从未看过     113\n",
      "有时会看到     91\n",
      "常常会看到     51\n",
      "0.0        0\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            167\n",
      "到该公司也不错        118\n",
      "不知道             90\n",
      "一定想办法到该公司工作     40\n",
      "0.0              1\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      154\n",
      "不知道      111\n",
      "买了也不错    100\n",
      "一定会买      50\n",
      "0.0        1\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     166\n",
      "一流      84\n",
      "三流      84\n",
      "不知道     81\n",
      "0.0      1\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道几项      129\n",
      "只知道名字      128\n",
      "大致了解       104\n",
      "一点印象都没有     33\n",
      "非常了解        18\n",
      "0.0          4\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "常常会看到    118\n",
      "有时会看到    112\n",
      "从未看过      45\n",
      "0.0        1\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        143\n",
      "不想去            141\n",
      "不知道             85\n",
      "一定想办法到该公司工作     44\n",
      "0.0              3\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    140\n",
      "不想买      117\n",
      "不知道      102\n",
      "一定会买      54\n",
      "0.0        3\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     198\n",
      "一流     131\n",
      "不知道     52\n",
      "三流      32\n",
      "0.0      3\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      150\n",
      "只知道名字      128\n",
      "大致了解        89\n",
      "一点印象都没有     38\n",
      "非常了解         6\n",
      "0.0          5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "偶尔看到     142\n",
      "有时会看到    127\n",
      "常常会看到     99\n",
      "从未看过      43\n",
      "0.0        4\n",
      "5.0        1\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            147\n",
      "到该公司也不错        145\n",
      "不知道             87\n",
      "一定想办法到该公司工作     33\n",
      "0.0              4\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      135\n",
      "不知道      121\n",
      "买了也不错    120\n",
      "一定会买      36\n",
      "0.0        4\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     170\n",
      "一流     126\n",
      "不知道     79\n",
      "三流      38\n",
      "0.0      3\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      148\n",
      "只知道几项      106\n",
      "一点印象都没有     80\n",
      "大致了解        68\n",
      "非常了解        12\n",
      "0.0          2\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "有时会看到    118\n",
      "从未看过      87\n",
      "常常会看到     70\n",
      "0.0        1\n",
      "5.0        0\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            161\n",
      "不知道            124\n",
      "到该公司也不错         97\n",
      "一定想办法到该公司工作     29\n",
      "0.0              5\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      149\n",
      "不想买      130\n",
      "买了也不错     95\n",
      "一定会买      38\n",
      "0.0        4\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     150\n",
      "一流     114\n",
      "不知道     90\n",
      "三流      58\n",
      "0.0      4\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 列出所有类别变量 \"\"\"\n",
    "for col in df5.columns[df5.dtypes=='category']:\n",
    "    print(df5[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性      211\n",
      "女性      204\n",
      "22.0      1\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     138\n",
      "九百到一千二    120\n",
      "一千二以上      85\n",
      "三百到六百      73\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       136\n",
      "德克士        78\n",
      "从不去快餐店     77\n",
      "麦当劳        62\n",
      "必胜客        44\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    188\n",
      "时尚新颖     91\n",
      "中西合璧     88\n",
      "古板单调     32\n",
      "其他类型     17\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般     224\n",
      "不错     139\n",
      "很好      33\n",
      "不好      17\n",
      "0.0      3\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    123\n",
      "只知道名字      120\n",
      "只知道几项       99\n",
      "大致了解        65\n",
      "非常了解         7\n",
      "0.0          2\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     161\n",
      "从未看过     113\n",
      "有时会看到     91\n",
      "常常会看到     51\n",
      "0.0        0\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            167\n",
      "到该公司也不错        118\n",
      "不知道             90\n",
      "一定想办法到该公司工作     40\n",
      "0.0              1\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      154\n",
      "不知道      111\n",
      "买了也不错    100\n",
      "一定会买      50\n",
      "0.0        1\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     166\n",
      "一流      84\n",
      "三流      84\n",
      "不知道     81\n",
      "0.0      1\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道几项      129\n",
      "只知道名字      128\n",
      "大致了解       104\n",
      "一点印象都没有     33\n",
      "非常了解        18\n",
      "0.0          4\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "常常会看到    118\n",
      "有时会看到    112\n",
      "从未看过      45\n",
      "0.0        1\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        143\n",
      "不想去            141\n",
      "不知道             85\n",
      "一定想办法到该公司工作     44\n",
      "0.0              3\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    140\n",
      "不想买      117\n",
      "不知道      102\n",
      "一定会买      54\n",
      "0.0        3\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     198\n",
      "一流     131\n",
      "不知道     52\n",
      "三流      32\n",
      "0.0      3\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      150\n",
      "只知道名字      128\n",
      "大致了解        89\n",
      "一点印象都没有     38\n",
      "非常了解         6\n",
      "0.0          5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "偶尔看到     142\n",
      "有时会看到    127\n",
      "常常会看到     99\n",
      "从未看过      43\n",
      "0.0        4\n",
      "5.0        1\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            147\n",
      "到该公司也不错        145\n",
      "不知道             87\n",
      "一定想办法到该公司工作     33\n",
      "0.0              4\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      135\n",
      "不知道      121\n",
      "买了也不错    120\n",
      "一定会买      36\n",
      "0.0        4\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     170\n",
      "一流     126\n",
      "不知道     79\n",
      "三流      38\n",
      "0.0      3\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      148\n",
      "只知道几项      106\n",
      "一点印象都没有     80\n",
      "大致了解        68\n",
      "非常了解        12\n",
      "0.0          2\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "有时会看到    118\n",
      "从未看过      87\n",
      "常常会看到     70\n",
      "0.0        1\n",
      "5.0        0\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            161\n",
      "不知道            124\n",
      "到该公司也不错         97\n",
      "一定想办法到该公司工作     29\n",
      "0.0              5\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      149\n",
      "不想买      130\n",
      "买了也不错     95\n",
      "一定会买      38\n",
      "0.0        4\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     150\n",
      "一流     114\n",
      "不知道     90\n",
      "三流      58\n",
      "0.0      4\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "### 使用自定义函数打印初全部类别变量取值\n",
    "mytools.print_all_cats(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 找出类别变量的异常值并修改\n",
    "df5.query('性别==22')\n",
    "df5.loc[321,'性别'] = '男性'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 找出类别变量的异常值并修改\n",
    "df5.query('德克士企业认知度==0')\n",
    "df5.loc[220,'德克士企业认知度'] = '只知道名字'\n",
    "df5.loc[357,'德克士企业认知度'] = '只知道名字'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 找出类别变量的异常值并修改\n",
    "df5.query('德克士就职意愿==0')\n",
    "df5.loc[223,'德克士就职意愿'] = '不知道'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 找出类别变量的异常值并修改\n",
    "df5.query('德克士股票==0')\n",
    "df5.loc[223,'德克士股票'] = '不知道'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       416.000000\n",
       "mean        513.247596\n",
       "std        4893.048164\n",
       "min           1.000000\n",
       "25%         140.750000\n",
       "50%         259.000000\n",
       "75%         392.000000\n",
       "max      100000.000000\n",
       "Name: 问卷编号, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 查看数值变量的异常值\n",
    "### 使用describe方法对数值变量进行描述统计，可得到最小值、最大值、方差等信息\n",
    "df5['问卷编号'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count       416.000000\n",
      "mean        513.247596\n",
      "std        4893.048164\n",
      "min           1.000000\n",
      "25%         140.750000\n",
      "50%         259.000000\n",
      "75%         392.000000\n",
      "max      100000.000000\n",
      "Name: 问卷编号, dtype: float64\n",
      "count    416.000000\n",
      "mean      24.127404\n",
      "std        6.501946\n",
      "min       10.000000\n",
      "25%       20.000000\n",
      "50%       22.000000\n",
      "75%       26.000000\n",
      "max       60.000000\n",
      "Name: 年龄, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.281250\n",
      "std        0.450151\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 欢乐聚餐, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.137019\n",
      "std        0.344282\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 快乐童年, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.293269\n",
      "std        0.455809\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 都市生活, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.170673\n",
      "std        0.598959\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max       10.000000\n",
      "Name: 洋溢青春, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.192308\n",
      "std        0.429669\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        4.000000\n",
      "Name: 其他感受, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.151442\n",
      "std        0.358911\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 健康安全, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.572115\n",
      "std        0.495368\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        1.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速便捷, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.314904\n",
      "std        0.465037\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 价格昂贵, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.165865\n",
      "std        0.372408\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 口感不好, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.163462\n",
      "std        0.370231\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 品类太少, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.076923\n",
      "std        0.266790\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他评价, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.456731\n",
      "std        0.498724\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速好吃, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.192308\n",
      "std        0.394588\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 服务好, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 种类多, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.168269\n",
      "std        0.374556\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 价格便宜, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.086538\n",
      "std        0.281496\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他吸引因素, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.319712\n",
      "std        0.466926\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 服务态度需改进, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 送餐速度, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.358173\n",
      "std        0.480041\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 就餐环境, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.391827\n",
      "std        0.488746\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 产品价格, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.237981\n",
      "std        0.426360\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 促销活动, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 产品种类, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.045673\n",
      "std        0.209026\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他改进因素, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "### 使用自定义函数对不同类型的数值变量进行描述统计\n",
    "mytools.print_all_int(df5)\n",
    "mytools.print_all_float(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 逻辑一致性清理\n",
    "### 构造逻辑判断表达式\n",
    "c1 = '德克士企业认知度 == \"一点印象都没有\" and 德克士就职意愿 == \"一定想办法到该公司工作\"'\n",
    "### 筛选出满足条件的数据\n",
    "temp = df5.query(c1)[['德克士企业认知度','德克士就职意愿']]\n",
    "### 删除满足上述条件的数据\n",
    "df6 = df5.drop(temp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "c2 = '伙食费 == \"三百到六百 \" and 常去的快餐店 == \"必胜客\"'\n",
    "### 筛选出满足条件的数据\n",
    "temp = df5.query(c2)[['伙食费','常去的快餐店']]\n",
    "df7 = df6.drop(temp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据清理完毕\n",
    "df = df7.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 描述统计\n",
    "## 单变量描述统计\n",
    "### 无序类别变量（定类变量）描述统计，\n",
    "### 可使用频数频率表、众数、柱状图等方式进行描述\n",
    "result = df['常去的快餐店'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>常去的快餐店</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>肯德基</td>\n",
       "      <td>127</td>\n",
       "      <td>32.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>从不去快餐店</td>\n",
       "      <td>73</td>\n",
       "      <td>18.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>德克士</td>\n",
       "      <td>68</td>\n",
       "      <td>17.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦当劳</td>\n",
       "      <td>57</td>\n",
       "      <td>14.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>必胜客</td>\n",
       "      <td>44</td>\n",
       "      <td>11.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>其他</td>\n",
       "      <td>18</td>\n",
       "      <td>4.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>总和</td>\n",
       "      <td>387</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   常去的快餐店   个数    百分比\n",
       "0     肯德基  127  32.82\n",
       "1  从不去快餐店   73  18.86\n",
       "2     德克士   68  17.57\n",
       "3     麦当劳   57  14.73\n",
       "4     必胜客   44  11.37\n",
       "5      其他   18   4.65\n",
       "6      总和  387  100.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.DataFrame()\n",
    "b['常去的快餐店'] = df['常去的快餐店'].value_counts().index\n",
    "b['个数'] = df['常去的快餐店'].value_counts().values\n",
    "b['百分比'] = df['常去的快餐店'].value_counts(normalize=True).values * 100\n",
    "b['百分比'] = b['百分比'].apply(lambda x: round(x, 2))\n",
    "total_row = pd.Series({'常去的快餐店':'总和','个数':b['个数'].sum(),'百分比':b['百分比'].sum()}).to_frame().T\n",
    "pd.concat([b,total_row],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>常去的快餐店</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>肯德基</td>\n",
       "      <td>127</td>\n",
       "      <td>32.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>从不去快餐店</td>\n",
       "      <td>73</td>\n",
       "      <td>18.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>德克士</td>\n",
       "      <td>68</td>\n",
       "      <td>17.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦当劳</td>\n",
       "      <td>57</td>\n",
       "      <td>14.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>必胜客</td>\n",
       "      <td>44</td>\n",
       "      <td>11.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>其他</td>\n",
       "      <td>18</td>\n",
       "      <td>4.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>总和</td>\n",
       "      <td>387</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   常去的快餐店   个数    百分比\n",
       "0     肯德基  127  32.82\n",
       "1  从不去快餐店   73  18.86\n",
       "2     德克士   68  17.57\n",
       "3     麦当劳   57  14.73\n",
       "4     必胜客   44  11.37\n",
       "5      其他   18   4.65\n",
       "6      总和  387  100.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df,'常去的快餐店')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>伙食费</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>六百到九百</td>\n",
       "      <td>125</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>九百到一千二</td>\n",
       "      <td>115</td>\n",
       "      <td>29.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>一千二以上</td>\n",
       "      <td>81</td>\n",
       "      <td>20.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>三百到六百</td>\n",
       "      <td>66</td>\n",
       "      <td>17.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>总和</td>\n",
       "      <td>387</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      伙食费   个数    百分比\n",
       "0   六百到九百  125   32.3\n",
       "1  九百到一千二  115  29.72\n",
       "2   一千二以上   81  20.93\n",
       "3   三百到六百   66  17.05\n",
       "4      总和  387  100.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df,'伙食费')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 调用外部自定义函数绘制柱状图\n",
    "mytools.show_bar(df,'常去的快餐店')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>德克士类型感知</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "      <th>累计百分比（%）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>普遍大众</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>时尚新颖</td>\n",
       "      <td>86</td>\n",
       "      <td>83.5</td>\n",
       "      <td>83.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中西合璧</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>古板单调</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>其他类型</td>\n",
       "      <td>17</td>\n",
       "      <td>16.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>总和</td>\n",
       "      <td>103</td>\n",
       "      <td>100.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  德克士类型感知   个数    百分比 累计百分比（%）\n",
       "0   普遍大众     0    0.0      0.0\n",
       "1    时尚新颖   86   83.5     83.5\n",
       "2   中西合璧     0    0.0     83.5\n",
       "3   古板单调     0    0.0     83.5\n",
       "4    其他类型   17   16.5    100.0\n",
       "5      总和  103  100.0         "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 单变量描述统计\n",
    "### 有序类别变量（定序变量）描述统计，\n",
    "### 可使用频数频率表（累计频率）、众数、柱状图等方式进行描述\n",
    "from pandas.api.types import CategoricalDtype\n",
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['普遍大众 ','时尚新颖', '中西合璧 ','古板单调 ', '其他类型'], ordered=True)\n",
    "df = df.astype({'德克士类型感知':cat_dtype})\n",
    "mytools.ordinal_desc(df,'德克士类型感知')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'德克士类型感知',sort=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    387.000000\n",
       "mean       7.651163\n",
       "std        2.140110\n",
       "min        3.000000\n",
       "25%        6.000000\n",
       "50%        8.000000\n",
       "75%        9.000000\n",
       "max       13.000000\n",
       "Name: 认知维度, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 单变量描述统计\n",
    "### 数值变量描述统计，\n",
    "### 可使用直方图、平均值、极差、四分位值等方式进行描述\n",
    "df['认知维度']= df['德克士广告接触度'].cat.codes + df['德克士企业认知度'].cat.codes + df['德克士综合评价'].cat.codes\n",
    "df['认知维度'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='认知维度', ylabel='Count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 绘制直方图\n",
    "import seaborn as sns\n",
    "sns.histplot(data=df, x=\"认知维度\",binwidth=1,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 描述统计\n",
    "## 双变量描述统计\n",
    "### 定类与定类\n",
    "result = pd.crosstab(\n",
    "        df['性别'],\n",
    "        df['伙食费'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>伙食费</th>\n",
       "      <th>一千二以上</th>\n",
       "      <th>三百到六百</th>\n",
       "      <th>九百到一千二</th>\n",
       "      <th>六百到九百</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>女性</th>\n",
       "      <td>44.44</td>\n",
       "      <td>57.58</td>\n",
       "      <td>46.96</td>\n",
       "      <td>46.4</td>\n",
       "      <td>48.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>男性</th>\n",
       "      <td>55.56</td>\n",
       "      <td>42.42</td>\n",
       "      <td>52.17</td>\n",
       "      <td>53.6</td>\n",
       "      <td>51.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "伙食费   一千二以上  三百到六百  九百到一千二  六百到九百     合计\n",
       "性别                                      \n",
       "22.0   0.00   0.00    0.87    0.0   0.26\n",
       "女性    44.44  57.58   46.96   46.4  48.06\n",
       "男性    55.56  42.42   52.17   53.6  51.68"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tau_y值为：0.004，该值属于极弱相关或不相关。'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "tau_y = mytools.goodmanKruska_tau_y(df, '性别', '伙食费')\n",
    "f'tau_y值为：{tau_y:.3f}，该值属于{mytools.draw_on_corr(tau_y)}。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>伙食费</th>\n",
       "      <th>三百到六百</th>\n",
       "      <th>六百到九百</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>常去的快餐店</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>从不去快餐店</th>\n",
       "      <td>33.33</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他</th>\n",
       "      <td>9.09</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士</th>\n",
       "      <td>27.27</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客</th>\n",
       "      <td>3.03</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基</th>\n",
       "      <td>15.15</td>\n",
       "      <td>36.0</td>\n",
       "      <td>28.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳</th>\n",
       "      <td>12.12</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "伙食费     三百到六百  六百到九百     合计\n",
       "常去的快餐店                     \n",
       "从不去快餐店  33.33   20.0  24.61\n",
       "其他       9.09    4.0   5.76\n",
       "德克士     27.27   20.0  22.51\n",
       "必胜客      3.03    8.0   6.28\n",
       "肯德基     15.15   36.0  28.80\n",
       "麦当劳     12.12   12.0  12.04"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 定序与定序\n",
    "### 一定要注意参与运算的数据必须为定序变量\n",
    "\n",
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['三百到六百','一千二以上 ', '九百到一千二 ','六百到九百'], ordered=True)\n",
    "df = df.astype({'伙食费':cat_dtype})\n",
    "\n",
    "result = pd.crosstab(\n",
    "        df['常去的快餐店'],\n",
    "        df['伙食费'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100\n",
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.1272162150330517"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "# 取得各个定序变量\n",
    "x = df['伙食费'].cat.codes\n",
    "y = df['常去的快餐店'].cat.codes\n",
    "dy = stats.somersd(x, y)\n",
    "# 打印萨莫司dy的值\n",
    "dy.statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'萨莫司dy值为：-0.127，该值属于极弱相关或不相关，p值为0.007，拒绝虚无假设，接收研究假设。'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = dy.pvalue\n",
    "f\"萨莫司dy值为：{dy.statistic:.3f}，该值属于{mytools.draw_on_corr(dy.statistic)}，p值为{p:.3f}，{mytools.p_result(p)['conclusion']}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 构造另一个定距变量\n",
    "df['情感维度']= df['德克士类型感知'].cat.codes + df['德克士综合评价'].cat.codes + df['德克士企业认知度'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 定距与定距\n",
    "mytools.gen_scatter(df,'情感维度','认知维度')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'积矩相关系数r为：0.659，决定系数r平方为：0.435，相关强度为中等。'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r, p = stats.pearsonr(df['情感维度'], df['认知维度'])\n",
    "f\"积矩相关系数r为：{r:.3f}，决定系数r平方为：{r*r:.3f}，相关强度为{mytools.draw_on_r(r*r)}。\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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msssuywUXXJDnnnsuQ4cOzUYbbdTXYwEAAAD9kOCyhAYPHpz3ve99fT0GAAAA0I+t9JcUAQAAAJQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFFbX1wMAAP3DjTfemLa2tr4eY7nX2tra/euYMWP6eJoVQ1NTUw444IC+HgMcJwtyrCzPsbL/EVwAgCRJW1tbWlpa+nqMFUa1WvX7CSsYx8nyHCtZkQkuAMBcKpVKmpub+3qM5VZbW1uq1WoqlUqampr6epzlWmtra6rVal+PAfNwnFx2jpXlOFb2X4ILADCX5ubmHHLIIX09BmTMmDF+8k2/5DhJf+JY2X+5aS4AAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYctdcJkxY0auuuqqvh4DAAAAYIF6PLhUq9V0dXXN9dq4ceMyduzYpdrf1KlTc/jhh+fd7353pkyZUmJEAAAAgKLqevoN3vWud6W9vT0zZszofu29731vWlpa0trausT7mz59epLk2WefzaqrrlpsTgAAAIBSejy4NDY2zvPawIEDU61Wl2p/48aNS5JsvvnmqVQqyzQbAAAAQE/o8UuKGhoa0tDQMNdr9fX1qa+vX+A2X//619PW1jbfZS+99FKSZLvttis2IwAAAEBJPR5cBgwYkPr6+kydOjVTp05NR0dHamtrU1tb271OZ2dn2tra8vbbbydJzjnnnGyzzTa5995759nfs88+m0qlkuHDh/f06AAAAABLpceDS6VSSW1tbY444ogMGTIkDQ0Nee655zJ+/Pju8DJgwIAMGjQoI0eO7N7u5Zdfzm677Zbvf//7c+3vr3/9a5Jkp5126unRAQAAAJZKrz0WulqtLtbHHOutt16am5vzzW9+M5/73OfS0dGRzs7O3HvvvRk0aJAzXAAAAIB+q9eCy0UXXZTXXnstb775ZjbffPOst956mTx5ciZPnpxJkybNs/7WW2+dv/zlL9l6661z1VVX5TOf+UzuuOOOTJkyJaNGjUpdXY/f7xcAAABgqfRatVh77bW7P6+pqUmlUsngwYMXus0mm2ySP//5z9l7771z00035bbbbkulUsmBBx7Y0+MCAAAALLUeO8Nl6tSpufTSS/PGG28s035WXXXV/O53v8vWW2+dadOmZfXVV88BBxxQaEoAAACA8nrkDJcvf/nL+dnPfpb29vZUq9VFnsmyKGPHjs2ECROSzL4Jb6VSKTEmAAAAQI/okTNc/v73v6etrS2VSmWZ77Xy/PPP52Mf+1gmTZqUoUOHZuLEifnlL39ZZlAAAACAHtAjweWYY47Jd7/73YwbNy4bbbRRkmTy5Ml56623Mn369O4nErW1taW1tTWtra3z3c+vf/3r7LTTTnn11Vdz9NFH5+abb061Ws0vfvGLnhgbAAAAoIgeuaTok5/85Dyvffvb386Pf/zjuV4bNGhQ9+f/epnQAw88kP322y+VSiXf//73c/LJJyeZ/fSiW2+9NS0tLRk4cGAPTA8AAACwbHrtsdBzzmpZ2Mc7TZ8+PVtssUX+9Kc/dceWJBk9enRmzpyZ3//+9701OgAAAMAS6bXgcuaZZ2bKlClpa2vLFltskfXWWy8zZsxIW1tbpk+fnjfffDN//OMfM3PmzCTJu9/97jz22GPZeeed59rPxz72sVSr1dx11129NToAAADAEumRS4rmZ5VVVun+fNasWZk1a1bq6+u7X2tubk4y+8yWJGlsbJxr+Rw77bRTKpVK7r333h6eGAAAAGDp9NoZLu8058yW+Wlvb0+SBd5Id9CgQdlwww3z1FNPpaurq8dmBAAAAFhavXaGyzttsskm3WHlXw0ZMiR///vf09DQsMDtt9xyy7zyyit57rnnsuWWW/bUmAAAAABLpVeCy8yZM3P//fcnmf00ovPOOy81NTV54oknUl9fn4aGhqyyyioZMmRIamtrFxlRVl999XzlK1/J+uuv3xvjAwAAACyRXgkub775ZkaOHLlY6w4cODDrrLNOhg0blmHDhmWbbbbJiBEjMnz48NTUzL4C6pxzzsl6663XkyMDAAAALLUeDy6DBw/OWmutlbq6utTV/fPturq60tXVlVmzZmXmzJlpa2tLe3t7pk+fnueeey7PPfdcktlnxMzZz+jRo3PwwQdnjz326OmxAQAAAJZajweXhx9+eLHX7ezszOTJkzNhwoS88soref755/Pwww/nj3/8Y8aNG5errroqY8aMycYbb5yLLrooH/vYx3pwcgAAAICl0yc3zV2Q2trarLHGGlljjTWy7bbbzrXswQcfzE9+8pNcc801efXVV7PNNtv00ZQAAAAAC9cnj4VeGiNGjMjPfvaz/OMf/8jFF1/shrkAAABAv7XcBJc51llnnRxxxBF9PQYAAADAAi13wQUAAACgv1tugsv++++fPffcs6/HAAAAAFikXrlp7tSpU3PMMcdk3XXXzbnnnrtU+7j33nszffr0wpMBAAAAlNcrZ7hUKpVce+21+cMf/jDX6+9973vneRrRW2+9lebm5uy8885zvT5gwIAMGDCgx2cFAAAAWFa9coZLU1NTkqShoWGu119//fW0t7fP9Vpzc3Pa29tTUzN3CxowYMA82wMAAAD0R70SXGpra5PMG1waGhpSrVbneS3JPGezDBgwILNmzerBKQEAAADK6NFLip5++un8x3/8RyqVSpJ5I0ptbW13jOke6P9/Zktd3dwtqFKpdO8HAAAAoD/rkTNcXnnllXzzm9/MNddck2q1mj322CPJ7ADz+c9/vnu9iRMnpqura67X5vj73/8+1+vjx4/Pqquu2hPjAgAAABTVI8HllltuyVVXXZUBAwbkqKOO6g4lEyZMyOWXXz7P+vN77bXXXpvndcEFAAAAWB70SHD5whe+kBdeeCFHHXVUNttss+7Xt9lmm5x22mlJkmq1mi9/+cuZNWtWLr744u51qtVqPv3pT2f77bfPt771re7Xjj/++J4YFQAAAKC4Hrtp7llnnTXPa0OHDs0nP/nJ7v99yimnZMaMGXO9Nseaa6451+vf+ta30tLS0jPDAgAAABTUIzfNnTZtWh599NGe2DUAAABAv9cjweV3v/tdhg8fnv333z+//vWv09HR0RNvAwAAANAv9cglRb/5zW/S2dmZm2++Of/7v/+bIUOGpFKp5Pnnn89Xv/rV7vXmPKXona/N8eyzz871+htvvJGOjo7u1yqVSn74wx/2xPgAAAAAy6RHgsupp56aUaNG5c4778zNN9+ct956K0ny6quv5sILL+xer1qtJkkuuOCCefbxyiuvzPf1Oa8JLgAAAEB/1SPBZdNNN82mm26aQw89NJdccknuvPPOXH755bn++uvT3t6eJFljjTVyzDHHZKONNlqsfVar1XR2dqazszMzZsxIW1tbT4y+ULfffnt++MMf5qGHHsqUKVOy5pprZrPNNsvhhx+ez33uc6mtre31mQAAAID+p8eeUjRHTU1NPvKRj+QjH/lILrzwwlx88cU5//zzM3HixJx11ln57Gc/m+9+97vZcMMN57t9tVrNM888k6FDh2b11Vfv6XEX6KKLLspxxx3XfVZOTU1NXn311bz66qu56667cu211+bmm29OU1NTn80IAAAA9A89ctPc+Wltbc0VV1yRU045Jc8//3yOPfbYdHV15Y477siqq67avd7kyZPn2q5SqWSnnXbKyJEje2vUeTzzzDP58pe/nPr6+lx66aWZNGlSZs2alVdeeSVnnXVW6urqcuutt7rECQAAAEjSi8HlhhtuyIknnpjhw4fnsccey49//OM88MAD+cUvfpF3vetd3etdfPHFWXvttfODH/yg+7V/+7d/y/PPP5+pU6f21rhzufbaa9PR0ZH/83/+T4466qgMGTIkNTU12WCDDXLyySfnS1/6UpLkV7/6VZ/MBwAAAPQvvRZcXnjhhQwcODCPPPJIdt1114wePTq1tbX5yEc+Mtd6m2++ed5444385S9/6X5tu+22S7VazQMPPNBb487l1Vdf7Z5tfuZcDtUX95UBAAAA+p8eCy777rtvnn322e7//e1vfztvvfVWrrzyyuy444757W9/mx133DGf+tSn8re//a17vc022yzJ7MdAz7HNNtukWq3mz3/+c0+Nu1DrrbdekuS3v/3tfJf/+te/TpLsuOOOvTYTAAAA0H/1SHB56qmn8qtf/Srbb799jjvuuLz44otJkvr6+hxyyCG58cYbkyRNTU258cYbs/322+fggw/OM8880x1cXn755e79bbXVVkmSu+++uyfGXaRDDjkkAwcOzC233JJjjz02zz33XNra2vL000/nyCOPzO23356mpqZ89atf7ZP5AAAAgP6lR4LLG2+8kY033jgzZ87MRRddlC222CKf/vSnc//99ydJ1llnndTU1GSvvfbK7bffnuHDh+faa6/Nv/3bv+WYY45Jc3NzXnzxxUyZMiVJsskmmyRJ7r333j65bGezzTbL7373uwwbNiwXX3xxtthiizQ3N2frrbfO5Zdfnq233jq33357tttuu8Xe57hx4xb6MX78+B78igAAAICe1COPhd5tt93ywgsv5NFHH821116bK664Itdff31uuOGGvP/9789Xv/rVrL766nnppZey22675b777ss111yTk046Kddcc02q1WoqlUoeeuih7L777tlggw2SJDNnzsxtt92WffbZpyfGXqipU6emvb19vsuam5uXOJDM+ZroHTfeeGOmTp2aWbNm9fUoCzTnkeOUValU+nqEBaqvr8+qq66aAw44oK9HgST/vBdZa2trxowZ08fTwOw/i4n75AGwfOqR4DLHe97znrznPe/Jd7/73dx00005//zzc9999+XTn/50qtXqXAHjoIMOyujRo3PQQQflt7/9barVah588MHsvvvuaWhoyBprrJG33norV1xxRa8HlzvvvDP77rtvOjs7kyTDhg3LBhtskBdeeCHjxo3LI488kk9+8pM5++yzc9JJJ/XqbCyetra2zJw5s6/HoA/055A1c+ZM30TQr8z576VaraalpaWPp4F/6s/HcgBYkB4NLt1vUleXAw88MAceeGB+85vf5Itf/GLGjh2blpaWPP7449l+++2TJKusskpuvPHGnHzyydlxxx2z9957d+9jnXXWyaRJkzJlypTuM2B6ywknnJDOzs6su+66ufrqq/PhD384SdLV1ZXrrrsuX/ziF/P222/n9NNPz1FHHZXVVlttkfscO3bsQpePHz8+I0aMKDI/s+8XNGvWLGe4rIT6+xkuTU1NfT0GdKtUKt1/xzY3N/f1OJDW1tZe/3cfAJTSK8Hlnfbee+888sgjufDCC3PEEUdk0003nWv5gAEDcv7558+z3QUXXJDtttsua6yxRm+NmmT2zXsfffTRJMmVV17ZHVuSpKamJgcddFCGDBmSPffcMy0tLfnTn/6U/fbbb5H7XX/99XtoYubHJRsAi9bU1JSWlpY0NzfnkEMO6etxIGPGjElLS4s4DcByqdeDS5KsscYaOeOMM5Zom912262Hplm4V199Ncnss28WNMPHP/7xrLLKKpk2bZqb3QIAAAA985SiFcnAgQOTzD71f3FOZx0yZEhPjwQAAAD0c71yhssPf/jDNDU1paGhIXV1dUt0HW61Ws3MmTMzY8aMzJw5MzvssEN23XXXnhv2X2y++eapq6vLpEmT8uqrr2a99dabZ52//OUvmTZtWpLkAx/4QK/NBgAAAPRPvRJcvv71r6erq6vIvk488cReDS7Nzc359Kc/nauvvjpnnnlmfvzjH8+1vKWlJccdd1yS5FOf+lQ23HDDXpsNAAAA6J967R4ugwYNyhZbbLHE2z3yyCNpbGzMtttumyTZYIMNSo+2SP/xH/+Rp556Kv/1X/+Vhx56KKNGjcqgQYPy0ksv5frrr8+kSZOy5ZZb5qKLLur12QAAAID+p9eCy7vf/e786U9/WuLtampqsvHGG+ehhx7qgakWz+qrr577778/l156aa6//vr85Cc/yZQpU9LU1JTNNtssX/7yl3P88cdn8ODBfTYjAAAA0H/0yVOKlkeNjY350pe+lC996Ut9PQoAAADQz3lKEQAAAEBhggsAAABAYYILAAAAQGG9dg+X119/PZdccslSbfv22293b1tTU5Ojjjqq5GgAAAAARfVacPnHP/6RY489dqm2nTBhQve2tbW1ggsAAADQr/VKcNl0003T2NiYxsbG1NXVpVKpLNH2M2fOzMyZMzNjxox0dXX10JQAAAAAZfRKcHn66acXuGzWrFm5+uqrU1NTk8MOO2yB602dOjWrrrpqT4wHAAAAUFSv3DR3ypQp2WuvvXL00UfPs2zWrFk58sgjc8wxxyxw+0suuSRbbbVVxo4d25NjAgAAABTRK8GltrY2t9xyS+666655ljU3NydJmpqa5rvtKaeckmOPPTavv/56Tj311B6dEwAAAKCEXgku9fX1C11eW1s733W+8Y1v5Oyzz06lUskPfvCDXHbZZT01IgAAAEAxvXIPl7q62W/T1dWVtra21NfXd7+WzA4uNTVzt5/LLrssZ511Vurr6/M///M/+cQnPtEbowIAAAAss14JLrW1tUmSF198MYMGDep+bdVVV83gwYMza9astLS05JJLLsm2226b6dOn59hjj01NTU1+9rOfiS0AAADAcqVXgss7VavVJElHR0cmTZqUSZMmJUmmTZuWY489dq51P/7xj+ezn/1sb48IAAAAsEx65B4u1Wo1F154Ydra2uZ6fdNNN83MmTMzbdq0vPnmm3nyySdzyy23pL6+Pk1NTXn/+9+fgQMHplqtplqt5pZbbsmmm26an//85z0xJgAAAECP6JHg8uyzz+aEE07IRhttlBNOOCEPPvhg97K6uroMHDgwq6++erbZZpt87GMfS01NTYYMGZJ77703b7/9dm699dZ8/vOfT0NDQ8aOHZujjz46H/7wh/PCCy/0xLgAAAAARfVIcHniiSdSW1ubiRMn5sILL8zOO++cSqWSyZMn5/77759n/TlntCRJTU1NPvrRj+anP/1pXnrppZx44ompr6/P3XffnR122CHXXXddT4wMAAAAUEyPBJdPfepTmTZtWv7whz/khBNOyIYbbphqtZq33norI0eOzIc+9KHcc8893et3dnams7Nznv2sueaa+cEPfpAnnngiu+66a6ZOnZqDDz44p5xySk+MDQAAAFBEjwSXJGlsbMzuu++eH/7wh3nxxRdzzz335NBDD019fX3uvffefPjDH86ECROSzA4us2bNWuC+Nt9889x+++35zne+kyT5wQ9+kD/96U89NToAAADAMum1pxR94AMfyAc+8IGcffbZOeuss/LrX/86a621VmbMmJEkaW9vX+j2lUol3/rWt7Leeuvlqaeeyi677NIbYwMAAAAssV5/LPQ666yTCy+8MKeddlqS2fdsufTSS1OpVBZr+89//vM9OB0AAADAsuv14DLHkCFDkiT19fX593//974aAwAAAKC4HruHS0+5++6709bW1tdjAAAAACzQchVcJk2alEMOOSTf+973+noUAAAAgAVaboLLzJkzs99++2XcuHH5/ve/n5tvvrmvRwIAAACYr+UiuEyfPj1777137rnnniTJZz/72ey77759PBUAAADA/PX74PLwww9n+PDhueOOO5IkRx55ZK688so+ngoAAABgwfptcHn55ZdzxBFHZOedd84zzzyTSqWS733ve/nZz36Wmpp+OzYAAABA3z0WekH++te/5ic/+Ukuv/zyzJo1K9VqNVtuuWV++tOfZuTIkX09HgAAAMAi9Xlw6erqysMPP5w777wz1113XR599NEkSbVazSabbJKTTjopn//851NX1+ejAgAAACyWHqkY9957b26++eYMGzYsgwcPTn19fbq6utLe3p6pU6fmjTfeyIsvvpgXX3wxTz75ZKZPn55kdmQZNGhQPvGJT+TQQw/NHnvskUql0hMjAgAAAPSYHgkuL730Us4999zFiiXVajVJsuWWW+bMM8/MXnvtlYaGhp4YCwAAAKBX9MjdZ1tbW1NbW5vm5uYMGTIka6yxRtZYY40MHjw4jY2NqVar3R9zPPvssznooIOyzz775Kqrrkp7e3tPjAYAAADQ43okuBx99NGZNWtWpk2blokTJ2bChAmZMGFCJk2alJaWlsyaNStvvfVWHn300dxwww057bTTMmLEiHR0dOS2227L5z73uay77ro5/fTTM3Xq1J4YEQAAAKDH9MnzlWtrazNkyJBsv/322X///XPqqafmvvvuy4QJE/L9738/66yzTt5+++185zvfyeabb57rrruuL8YEAAAAWCp9ElwWZPXVV8/JJ5+cF198MRdddFEGDx6cN998MwcffHAOO+ywtLW19fWIAAAAAIvUr4LLHPX19fk//+f/5Kmnnsr++++farWaq6++Oh/4wAfy2muv9fV4AAAAAAvVL4PLAw88kKOOOipDhgzJDTfckPPPPz+VSiWPP/54dtpppzzzzDN9PSIAAADAAvW74PLwww/nQx/6UC677LLsueeeaW1tzVe+8pVcf/31aWxszKuvvppdd901zz//fF+PCgAAADBfPR5cZs6cmcMOOywPP/zwYq3/vve9LzfeeGOamprypz/9KaNGjcq0adOy33775X/+539SU1OTN954I3/+8597eHIAAACApdOjwaWtrS2jR4/O1VdfnX322Sdjx45drO1Gjx6d2267LUOGDMn999+fj3zkI5k4cWL22muvnHXWWTn00ENz+OGH9+ToAAAAAEutrqd23Nramj333DN33313ampqcvzxx2eDDTboXn711VensbExjY2NaWhoSKVSmWcfp556ak488cT85S9/yfvf//789re/zde+9rVMmzatp8YGAAAAWGY9Elw6Ojqy//775+67705TU1Ouu+66jB49eq51Dj300PlGlgV54YUXMmLEiFx//fXZfffdS48MAAAAUEyPBJepU6dm2rRpqa2tzbXXXjtPbJmjpqYma6yxxiL3N3ny5MycOTOrrbZatt5669LjAgAAABTVI8FltdVWy+9+97vceeed2WeffRa43sYbb5znnntukfvr6OjI5Zdfnt122y3rrbdeyVEBAAAAiuuxe7gMHjw4++6773yXdXR0JEk6OzsXa191dXU56qijis0GAAAA0JN6/LHQ89Pa2pokmTFjRl+8PQAAAECP6rEzXBamUqnkC1/4Qpqamvri7QEAAAB6VJ8El1VWWSUXX3xxX7w1AAAAQI/rk0uKAAAAAFZkggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYXV9PQAAAMDyoK2tLUnS2tqaMWPG9PE0MFtra2uSf/75pP8QXAAAABZDtVrt/rWlpaWPp4G5zfnzSf8huAAAACyGSqWSarWaSqWS5ubmvh4Hksw+w2XOn0v6F8EFAABgMTQ1NaWlpSXNzc055JBD+nocSJKMGTMmLS0taWpq6utR+BdumgsAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgshSq1Wo+9rGPpVKpZJdddklXV1dfjwQAAAD0I4LLUvjxj3+cW2+9NQMHDsxll12Wmhq/jQAAAMA/KQVL6Nlnn83JJ5+cJDnnnHOy6aab9vFEAAAAQH8juCyBzs7OHH744Wltbc3uu++eY489tq9HAgAAAPohwWUJfP/7388DDzyQVVddNT/72c9SqVT6eiQAAACgHxJcFtNf//rXnH766UmSCy64IBtuuGEfTwQAAAD0V3V9PcDyYObMmTn88MMza9asDBs2LJMmTcrxxx+f+vr6jBgxIvvuu28GDBiwRPscN27cQpePHz9+WUYGAAAA+pDgshjOPffcPPnkk0lmh5Bf/vKXaW5uzuOPP55zzz03W2yxRa6++ursuOOOi73PDTbYoKfGBYBl0tramjFjxvT1GMuttra2VKvVVCqVNDU19fU4y7XW1ta+HgHmy3Fy2TlWluNY2X8JLovw+uuv58wzz0yS7Lzzzrnxxhuz9tprJ0m6urpy8cUX5ytf+Up222233Hfffdl22237clwAWGbVajUtLS19PcZyz+8jrLj8912O30tWZILLIvzXf/1XWlpaUldXl+uuu647tiRJTU1NvvjFL+b555/P+eefn29/+9u5/vrrF2u/Y8eOXejy8ePHZ8SIEcs0OwAsCT9hLKO1tbX7p7bNzc19Pc4KwZ9N+gt/FstxrCzPn8/+R3BZhDvuuCNJstdeey3wMqADDjgg559/fn7961+nq6srNTWLvhfx+uuvX3ROAFhWBxxwQF+PsEIYM2ZMWlpa0tzcnEMOOaSvxwEKcpwsx7GSlYGnFC3Cm2++mSTZaaedFrjOWmutlSSZMWNG3njjjV6ZCwAAAOi/BJdFWHXVVZMk73rXuxa4zrRp07o/HzhwYE+PBAAAAPRzgssizLkJ7tNPP73Ade6///4kyXrrrZdVVlmlV+YCAAAA+i/BZRH23nvvJMkVV1yR1157bZ7l06ZNy/nnn58k2WeffXp1NgAAAKB/ElwW4ZOf/GTe+973ZurUqfnYxz6W22+/PbNmzUpHR0fuuOOO7LLLLnn++efT2NiYk046qa/HBQAAAPoBTylahJqamlx//fXZd9998+STT2b33XdPpVJJMvuZ8UlSW1ubMWPGZNiwYX05KgAAANBPOMNlMWyyySZ54IEHcskll2S33XbLuuuum7q6uqy++uoZPXp07rrrLo+IAwAAALo5w2UxNTc35+ijj87RRx/d16MAAAAA/ZwzXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1yW0QMPPJABAwZk11137etRAAAAgH5CcFkGU6ZMyUEHHZRZs2b19SgAAABAPyK4LIMvfOELefHFF/t6DAAAAKCfEVyW0qWXXpr//u//TqVS6etRAAAAgH5GcFkKf/vb33L88cenUqnkxBNP7OtxAAAAgH5GcFlC7e3t+cxnPpPW1tZ87Wtfy957793XIwEAAAD9jOCyhI4//vg8+eST+cAHPpAzzzyzr8cBAAAA+qG6vh5geXL99dfnJz/5SVZfffVcd911qatb+t++cePGLXT5+PHjl3rfAAAAQN8SXBbTyy+/nKOPPjqVSiW/+MUvsv766y/T/jbYYINCkwEAAAD9jUuKFkNHR0cOOuigvP322znppJOy11579fVIAAAAQD/mDJfF8K1vfSv33XdfRo4cme9+97tF9jl27NiFLh8/fnxGjBhR5L0AAACA3iW4LMJtt92Ws88+O2ussUauvfbaZbpvyzst6yVJAAAAQP/lkqJFuOqqq1KtVjNx4sRssMEGqVQqc33stttuSZK77rqr+7XLL7+8b4cGAAAA+pQzXBahvr4+DQ0NC1ze1dWVWbNmpVKpZMCAAUmS2tra3hoPAAAA6Iec4bIIl156adrb2xf48Yc//CFJsssuu3S/dthhh/Xx1AAAAEBfElwAAAAAChNcAAAAAAoTXAAAAAAKc9PcZbTrrrumWq329RgAAABAP+IMFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwur6egAAgMV14403pq2tra/HWKjW1tbuX8eMGdPH0yxaU1NTDjjggL4eAyjIsbIsx0mWluACACw32tra0tLS0tdjLJZqtbrczAqsWBwroX8QXACA5UZTU1Nfj7BIbW1tqVarqVQqy8W8y8OMwJJZHv67Xp6Olf19PvovwQUAWG44pRtg0RwroX9w01wAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMLq+noA5q+jo6P78/Hjx/fhJAAAALDieuf33O/8XnxZCS791Jtvvtn9+YgRI/pwEgAAAFg5vPnmm9l4442L7MslRQAAAACFVarVarWvh2Be7e3teeKJJ5IkQ4cOTV2dk5Hoe+PHj+8+4+rBBx/MOuus08cTAfQvjpMAi+ZYSX/T0dHRfZXJdtttl8bGxiL79V18P9XY2Jjhw4f39RiwQOuss07WX3/9vh4DoN9ynARYNMdK+otSlxG9k0uKAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAqrVKvVal8PAQAAALAicYYLAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggus4Lq6utLR0bFE23R2di7xNgArkltvvTUzZswotr/W1tbcdtttue2229Le3l5svwArEsdKVjSCC6yAHnjggey3335ZffXVU1tbm/r6+my66aY544wzFviX1+9///uMGjUqgwcPTl1dXRoaGrLtttvmRz/6Ubq6uorP+I9//COHHXZY1lprrTQ2NuY973lPrrzyyuLvA7CkpkyZkj322CNrrrlmvvKVrxTZ54QJEzJq1KiMGjUqkyZNWqxt9t5776y99tr50Y9+VGQGgCU1YcKEbLXVVsU+dt9990W+n2MlK5K6vh4AKOuKK67Iv//7v6ezszPNzc15//vfnzfeeCMvvPBCTj311Nxyyy2544470tDQ0L3Nd7/73XzrW99KkgwePDgf+MAH8vLLL+dvf/tbvvSlL+Wuu+7K//zP/xSb8a9//Wt23XXXTJ06NUlSqVTy2GOP5fDDD8/zzz+f73znO8XeC2BJPfTQQ6lWq5k6dWre/e53F9nnwIEDuz8fMGDAYm0zffr0TJgwwRmHQJ8ZMGBAnnnmmWL7q6tb+LefjpWsaJzhAiuQxx57LF/4whfS2dmZE088MRMmTMh9992X559/vrv6//nPf84PfvCD7m1+97vf5Vvf+lZqa2tzzjnn5I033si9996bl156KV//+teTJNdff32uuuqqIjNOnjw5e+65Z6ZOnZphw4blrrvuSkdHRx588MFstNFG+e53v5v77ruvyHsBLI0777wzSbL66qvn0EMPLbLP+vr67s/fGbznOPPMM/OJT3wiN9xwwzzrzW99gN7Q2NjY/fmtt96aarU6z8cf//jH7nWee+65+a7z7W9/O8mij2eOlaxoBBdYgZx00kmZOXNmTjrppJx77rkZNGhQktlnkBx33HHZd999kyTXXHNNkqRarearX/1qkuQ///M/8//+3//r/mlCXV1dvv/97+c973nPXNssqzPPPDMTJkxIU1NTfv/732eXXXZJTU1Nhg8fniuvvDJdXV3FTuEHWBq33HJLkuSggw5a7J+wLkptbW335/Pb5913351f/epX+dvf/tb92pxvdN75DQ9Ab3rnsauERZ3h4ljJikZwgRVES0tLmpubM3z48Jx66qnzXWf48OFJktdeey1JMnbs2GyyySYZNWpUjjnmmPlu8773vW+ubZZFR0dHfvrTnyZJjjrqqGy++eZzLf/Qhz6U4cOH56GHHsorr7yyzO8HsKReeumlPPLII0lS7OyWJKmp+ec/uWprazN9+vS5lj/22GNJkl122WWebSqVSrE5AJbEO49dJSzqeOZYyYpGcIEVxMCBA3PTTTflwQcfnOv613eaE03mLN9www3zm9/8Jn/4wx8W+JfUv26zLJ544om8/fbbSZJPf/rT811nr732SvLPnzAD9KY5p6lvttlm2WmnnZZ5fy+88MI8Nx6/5pprsuGGG3Yf5958882MHz8+lUol733vexe6v46OjrS0tGTixInLPBvAkhg1alQqlco8H7vttlv3Optvvvl811nU/fkcK1lRuWkurCQ6Oztz8803J0k++tGPLtY2b7/9dm6//fYl2mZhnn/++SSzf2Ix52ybf7X99tsnSZ5++ullfj+A+XnjjTfS1NSU+vr61NXVpba2tjs6zwkuBx988BLts1qtZsaMGZkxY0YGDx6cJJk4cWI23XTTbLjhhnnqqae611199dUzefLknH/++fn4xz/efUZNtVrt3vadjjzyyBx55JFzvbbWWmvl9ddfX6IZAZbFBhtskObm5nleb21tzdixY5Mkm2yyyVz3YZlj4sSJeeutt+a7X8dKVmSCC6wkfv7zn+fVV19Nkhx//PGLtc0PfvCDzJgxI42NjQu85GhJzPmLdqONNlrgjc3WXHPNJLNP6wfoCeuuu246OzsXus7pp5+e008/fYn3/eEPf7j7prtzwvHGG2881zqjRo3KJptskltvvTVjx47NHXfckSRZbbXVMmTIkO71Xn/99bS0tGTNNdfMKquskiSZNWtWZsyY0X2PLoDe8vOf/3y+j3W+8847u89y+f3vf5/NNttsnnVOO+20BZ7l4ljJikxwgZXA+PHju584dOSRRy7yNMxk9uU/5557bpLklFNOyTrrrLPMc8yYMSNJ5vpL8l/NWTZ+/Phlfj+A+Vl11VXT2NiYxsbGue4XMGnSpEyePDl1dXXZaKONFrj9W2+9lbfffjuVSiWbbLJJkqSrq2uef9zPeZTqtttuO9f2lUolhx56aE4//fRcddVVuf7665Mkl1xyST75yU92r7fffvvl5ptvztlnn50jjjhimb9ugCX1r5f5LKtqtTrPa46VrMgEF1jBdXV15fDDD8+kSZOy0UYb5bzzzlvkNq2trTnooIMyc+bMvO997+uONctqzp3n53c66hxz7kjf1tZW5D0B/tWkSZPm+/rHP/7x/P73v8/BBx+cK664YoHbf/azn811112XHXbYIQ8//PAC13v88ceT/PNSyXc67LDD8tprr6W2tjYvvvhiBg8e3H0PK4D+or29vcf351jJikxwgRXct771rdx2221paGjIddddl3e9612L3Oboo4/OU089ldVWWy3XXXddsceiNjU1JZn/Y/7mmPPT5tbW1iLvCbA4Xn/99dx2221Jki9/+csLXfe+++5Lkuy5554LXe/uu+9Okuywww7zLNtss81y6aWXdn/j8PnPf777GAnQX7zzB2CjRo1a5Pr/+gTKhe1vDsdKVmSCC6zArrvuupx55plJkosuumixnrhxzjnn5Oqrr05tbW2uu+667tPlS5hzudCce8nMz+TJk5PM/5RTgJ4yZsyYdHZ2Zuedd86OO+64wPWee+657sfW77333gtcb9q0aXn88cczYMCAvPvd786sWbPmWae9vT1DhgzJ4MGDc8IJJyz7FwFQWGNjY04++eQFLn/rrbcyadKkrLLKKll99dXne8Pcd/rXy8odK1nRCS6wgrrvvvu679R+8sknz3PX9vm58cYbc8oppyRJfvSjH833xmjLYsMNN0wy+4a41Wp1vo+injBhQpK4yRnQa7q6unLppZcmWfTZLXOeYrTpppvm/e9//wLXa25uzoMPPpiXX345DQ0N8/0morGxMWPGjOk+xX5Bx8V/nXXO+gu7PBOghMGDB+ess85a4PILLrigO4LcdtttS/xUS8dKVnQ1i14FWN489dRT2WeffdLW1pYDDzww3//+9xe5zZ133plDDjkkXV1d+drXvlbkqUT/atttt01DQ0Pa29vneuzfO91///1J/hlnAHraDTfckGeeeSbrrrvuXDdinJ+rr746SXLooYcudL3a2trssMMO2X///Rf5/o2Njdl1111TU1OT+vr67hv6/upXv0oy+zLPxsbGNDQ0pLa2NgMHDsxJJ520mF8dQO+Y3yVBi+JYyYrOGS6wgnnyySez++6756233sqHP/zhXHnllYv8KcCdd96ZffbZJ+3t7TnooINyzjnn9MhsTU1N+eAHP5jbb7891157bb773e/Os86tt96aZOn+0gZYGnOi9IYbbpgXX3wxW2yxxXzXu/POO/PEE0+kvr4+Rx11VNEZ6urqMnjw4DQ0NHSfkv/WW2+lvb09q6yySpqbm7t/YtvW1jbX05UAesIll1ySU089NY2Njamrm/+3jVOmTEky+4lCw4cPX6z9Dh06tPteWEvKsZLljeACK5AJEyZk9913z4QJE7Lddtvl5ptvTkNDw0K3efLJJzN69Oi0tLRk9913z+WXX77IQLMsjjjiiNx+++350Y9+lGOOOSbrr79+97I77rgjDzzwQJJk9OjRPTYDwByvvvpq900c77///my11VbZfffd88UvfjH77LNP99PVkuQHP/hBkuSggw6a69hVwj333DPPa3MedXreeed51CnQ62bOnNl9qfeiVKvV/OMf/1isdYcOHbrUMzlWsryR/GAFcu6553b/xfj3v/89a621Vvfplv/68ac//SlJ8p3vfCctLS1JkrvuuiurrrrqArd5+eWXu9/r5Zdf7n79yiuvXOwZDzzwwAwbNixTpkzJ7rvvnnvvvTft7e256aabcuCBByZJdtlll4XetBKglPXWWy9///vfc8899+Rzn/tcGhoacuutt2b//ffPsGHD8r3vfS8TJkzI3Xffnd/+9repr6/PN77xjb4eG6DHff7zn8/UqVMza9asdHV1pVqtzvOx8847J0lOOeWU+S5/58ecf+eVfCAD9HfOcIEVyKOPPtr9eUdHRzo6Oha4bldX1zzbzO9GZe/0zicHVavVzJgxI0nS2dm52DM2NDTk+uuvz+67755nnnkmH/zgB+davtpqq3XfvBKgt4wcOTIjR47Meeedl5///Of5j//4j4wdOzbf/OY3c/rpp3c/WeO4447Llltu2cfTAvS8Rd1sduLEiXn44YeTJCNGjFjk/t58880ks0M3rCyc4QIrkFtvvXWRP12Y87Hrrrsmmf2I08XdZuONN+5+r4033rj79SU9fXOHHXbII488kv3222+ua2tHjRqVBx54YIH3TwDoaauttlq+9rWv5YUXXsgvfvGLbL755nOdVv/000/nL3/5Sx9PCdD3/uu//iuzZs3KoEGDssceeyxy/ddffz2J4MLKRXAB+sSwYcNy00035fXXX8+DDz6Y1157LX/4wx+y2Wab9fVoAKmrq8sBBxyQbbfdNkm67+Vyyy23ZMcdd8zo0aO7f7ILsLK54447cuaZZyZJvvjFL6apqWmR24wbNy5Jsu666/bobNCfCC5Anxo6dGiGDx+eddZZp69HAUgy+5LJ66+/Pttss01++ctfprGxMTfccEMeeOCB7LXXXkmS3/zmNxk+fHj23XffPP7444u1T4Dl3YwZM3LOOedkjz32yIwZM7Ltttvmm9/85iK3e+211zJ9+vQks3/otiCOlaxoBBcAgCTjx4/P+eefn6233joHHnhgXnnllbz3ve/NAw88kH333TcjRozIb37zmzz44IPdl2X+7//+b9773vfm0EMPnevG4v9q5syZ8/184sSJmTx5clpbW7vvrbUw1Wo1bW1tmTx5cvf9EAB62t/+9rd873vfyyabbJKTTz45HR0ded/73pff//73WWWVVRa5/ZgxY5LMPltwm222WeB6jpWsaCpVGREAWMl0dXXlxRdfzCOPPJL77rsvd955Zx577LHun65uvPHG+f/+v/8vRx555FyPhn6nX//61/nKV76SF154IUnS2NiYRx55ZL7fTIwfP777NPrJkyfnXe96V5Jkq622yjPPPLNUX8NGG22Ul156aam2BViYl19+OZdddlkeffTR3H///XM9HnrttdfOiSeemK985Supr6+f7/bTpk3LN77xjbzxxht57rnn8uijj3bfQ/CPf/zjAt/XsZIVjacUAQArlbfffju77rprHnvssbleb2xszCc+8Ykcdthh+fjHP566uoX/M2n06NH56Ec/mtNPPz3nnHNOPvWpTy3wJ7cL+qntsGHDMmDAgDQ0NKSxsXGBcWeOrq6utLe3p729PUOHDl3UlwqwVDbaaKNMnjw5N998c5JkwIAB2WOPPXLwwQdnv/32S2Nj40K3X2WVVTJ06ND86Ec/6n5t8803z49//OOFbudYyYrGGS4AwErn2WefzYgRI7Lttttmp512yvvf//7sscceGTx48FLt76677sqmm26a9ddff77Ln3vuue4nsI0dO3aB6wH0Fx0dHTnuuOOyyy67ZPTo0Ut8fGxpackXvvCFjBgxIiNHjswOO+ww19Mp58exkhWN4AIArJSq1WoqlUpfjwEArKDcNBcAWCmJLQBATxJcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAACqvr6wEAAPrK3XffnRkzZmTbbbfNOuus09fjLNKECRPy97//PQ0NDdl55537ehwAYCEq1Wq12tdDAAD0hY022iivvPJKxowZk4MPPniR63/ve9/Lf/7nf+YjH/lIrr766sV6j1/84hd55ZVXFrneoEGDcvzxxy90nf/+7//OZz7zmWy++eZ59tlnF+v9AYC+4QwXAGClNXDgwCTJgAEDFmv9mTNnZsKECWltbV3s9xgzZkz+8Ic/LHK9YcOGLTK4DBo0KEnS3Ny82O8PAPQNwQUAWGnV19cnSRoaGuZZdvvtt+c73/lO9txzz5xyyilzrTe/9ReksbExSbLffvtlxx13nGf5iy++mJ///Ofd683R2tqa1tbWDBgwIKuuumqSpK6ubq5fAYD+y9/WAMBKq7a2Nsn8z3B5+OGHc/fdd6e+vr47uMyJIv8aRxbnPfbee+8cddRR8yy/88478/Of/7w7/sxx5pln5nvf+15Gjx6dX/3qV0mSmhrPOwCA5YW/tQGAldacgFFbW5uurq65LhV67LHHkiS77LLLPOtXKpUlfo9F+dd9rrLKKnP9CgAsXwQXAGCl8sorr2TGjBlzvfbEE09kq622yumnn9792pzgMr/LgN6pq6sr7e3tmThxYjo6OuZZvrTPJ5hzxsu/nvkCACwfBBcAYKWy1157ZeDAgXnuuee6X9tggw3y6quv5pJLLklLS0umT5+eZ555Jkmyzz77pFKppFKp5IQTTkiSXHHFFd2v1dbWpqmpKUOHDs3zzz8/z/t1dXUt1lz/GmaW5mwaAKD/cA8XAGCl0dnZmeeffz6VSiUbb7xx9+vvete78pnPfCaXXXZZbrrppqy22mrp7OxMU1NT1l133e71pkyZkokTJ2aVVVbJmmuu2b3PmTNnZvr06fO9fGjWrFlJktNPPz0/+tGP5lk+ffr0JLOfgAQArDgEFwBgpfHSSy9lxowZ2Xrrree5VOewww7LZZddlquuuirrrLNOkuTf//3f85//+Z/d61xwwQU54YQTcsABB+Tyyy9frPecc1+YsWPHZuzYsQtcr62tbQm/GgCgPxNcAICVxuOPP54k2X777edZtuuuu+Zzn/tcPvKRj+TYY49Nkhx88MHL/J6XXXZZd0wZP358zj///AwbNix77bVXNtpoo+71POoZAFYs/mYHAFYad999d5Jkhx12mGdZpVLJ5ZdfnnPOOSetra1573vfm5133nmZ3/OdUaWxsbH7Ec+f/OQns9VWWy3z/gGA/slNcwGAlcY999yTJBkxYsQC15k5c2bWXnvtnHTSScXfv7m5ufvzzTbbrPj+AYD+wxkuAMBK4+qrr85DDz2UnXbaaYHrfPOb38xJJ52Uzs7OdHZ2pra2dpH7bW9vz6xZs7LKKqt0vzZlypQ899xzaW5uTmNjY2pqajJlypTu5dOnT89LL700z76GDh2agQMHLtkXBgD0O4ILALDS2GyzzRbrzJIBAwbk61//es4+++zU1dV1R5fOzs4kyZVXXplrr702yewzYqrVarbddts8+eST3ft46KGHMmrUqAW+x5Zbbjnf12+44YYccMABi/01AQD9k+ACADAfNTU1GThwYJqbm1NfX59KpZLp06dnypQpaWxszJAhQ5LMPrulra1tnjNh6urqMnTo0AwaNKh7+5aWlowbNy51dXXZdNNN51p/7NixaW1tzaBBg3rtawQAeo57uAAAzMeZZ56Z6dOn54033sirr76acePG5bTTTkuSHHjggRk3blzGjRuXiRMnpqWlJY899thc2++6665544038sILL+SZZ57J008/nbPPPjvJ7KckPf3003N97Ljjjkn++bSiarXae18sAFCc4AIA0Euee+65JMnmm28+z7LW1tYksy9nSpKOjo65fgUAli+CCwBAL7ntttuSZL437W1ra0vyzycZzZw5c65fAYDli+ACANALHn/88e7HUu+1117zLJ9zhsucJxS1tLQkmf00IwBg+SO4AAD0sM7Ozhx33HFJkpEjR873CUX/eobLmWeemWq1mt/+9re9NygAUIzgAgCstHrjxrTVajX/9//+39xzzz2pVCo588wz57venDNa5gSXBe0LAFg+eCw0ALDSmt99UqZMmZKOjo40NjamoaGh+6lBCzNjxoy0t7envb09a6yxRvcjotva2nLMMcfkF7/4RZLkG9/4RnbZZZd5tn/00Ue7Lx1a2GOhn3nmmSRZrJkAgL7lb2sAYKU1v+By8skn5yc/+clCt7viiityxRVXzHfZiy++mI033jj33Xdfjj766Dz11FNJks9//vM544wzutf785//nFNPPTWTJ0/uXmerrbZKQ0PDXPu7+uqr8/jjj+e1117LjTfemCRZa621lvArBQB6m+ACAKy05hdc1lprrWy77bZpaGhIY2Nj6uvrF7mfOWe3tLe3p76+Pp2dnfnOd77THVKOP/74nHfeealUKt3b7LjjjvnLX/6SyZMnJ0mGDh2aH//4x/Pd/9lnnz3X//7sZz+7ZF8oANDrKlUXAwMAK6n11lsvr732Wq688soceuihRffd1taWD37wgznmmGNy9NFHz3edM844I2+++WZGjRqVPfbYIwMGDJhnndbW1myyySbZaqutMnLkyOy///553/veV3RWAKA8wQUAoId0dnZ2388FAFi5CC4AAAAAhXksNAAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYf8/7pFddtfNgPUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 定类与定距\n",
    "\n",
    "sns.boxplot(\n",
    "        x='性别',\n",
    "        y='认知维度',\n",
    "        data=df,\n",
    "        color='white',\n",
    "        linewidth=1,\n",
    "        width=0.5,\n",
    "    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'相关比率为：0.017，按照J.Cohen 提出的标准(0.01时为小效应，0.06时为中等效应，而0.14为大效应)，强度为低度相关。'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算相关比率\n",
    "from statsmodels.formula.api import ols\n",
    "model = ols('认知维度 ~ 性别', df).fit()\n",
    "eta_2 = model.rsquared\n",
    "f\"\"\"相关比率为：{eta_2:.3f}，按照J.Cohen 提出的标准(0.01时为小效应，0.06时为中等效应，而0.14为大效应)，强度为{mytools.draw_on_eta2(eta_2)}。\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = mytools.set_label_to_code(df, metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "mytools.to_sav(df_clean,metadata,'.企业形象调查数据处理结果.sav')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "09738f47427dba2c99ec68f9759744be2c477c434791ae3d2ff19909b29a18a8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
